{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "import sys\n",
        "import asyncio\n",
        "import json\n",
        "from typing import Dict, List, Any, Optional\n",
        "from datetime import datetime\n",
        "import os\n",
        "\n",
        "def install_packages():\n",
        "    packages = [\n",
        "        \"beeai-framework\",\n",
        "        \"requests\",\n",
        "        \"beautifulsoup4\",\n",
        "        \"numpy\",\n",
        "        \"pandas\",\n",
        "        \"pydantic\"\n",
        "    ]\n",
        "\n",
        "    print(\"Installing required packages...\")\n",
        "    for package in packages:\n",
        "        try:\n",
        "            subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
        "            print(f\"✅ {package} installed successfully\")\n",
        "        except subprocess.CalledProcessError as e:\n",
        "            print(f\"❌ Failed to install {package}: {e}\")\n",
        "    print(\"Installation complete!\")\n",
        "\n",
        "install_packages()\n",
        "\n",
        "try:\n",
        "    from beeai_framework import ChatModel\n",
        "    from beeai_framework.agents import Agent\n",
        "    from beeai_framework.tools import Tool\n",
        "    from beeai_framework.workflows import Workflow\n",
        "    BEEAI_AVAILABLE = True\n",
        "    print(\"✅ BeeAI Framework imported successfully\")\n",
        "except ImportError as e:\n",
        "    print(f\"⚠️ BeeAI Framework import failed: {e}\")\n",
        "    print(\"Falling back to custom implementation...\")\n",
        "    BEEAI_AVAILABLE = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "db2bYrgxIfDH",
        "outputId": "65579f04-1538-45b5-e494-3d8abe928d72"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing required packages...\n",
            "✅ beeai-framework installed successfully\n",
            "✅ requests installed successfully\n",
            "✅ beautifulsoup4 installed successfully\n",
            "✅ numpy installed successfully\n",
            "✅ pandas installed successfully\n",
            "✅ pydantic installed successfully\n",
            "Installation complete!\n",
            "⚠️ BeeAI Framework import failed: cannot import name 'ChatModel' from 'beeai_framework' (/usr/local/lib/python3.11/dist-packages/beeai_framework/__init__.py)\n",
            "Falling back to custom implementation...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class MockChatModel:\n",
        "    \"\"\"Mock LLM for demonstration purposes\"\"\"\n",
        "    def __init__(self, model_name: str = \"mock-llm\"):\n",
        "        self.model_name = model_name\n",
        "\n",
        "    async def generate(self, messages: List[Dict[str, str]]) -> str:\n",
        "        \"\"\"Generate a mock response\"\"\"\n",
        "        last_message = messages[-1]['content'] if messages else \"\"\n",
        "\n",
        "        if \"market\" in last_message.lower():\n",
        "            return \"Market analysis shows strong growth in AI frameworks with 42% YoY increase. Key competitors include LangChain, CrewAI, and AutoGen.\"\n",
        "        elif \"code\" in last_message.lower():\n",
        "            return \"Code analysis reveals good structure with async patterns. Consider adding more error handling and documentation.\"\n",
        "        elif \"strategy\" in last_message.lower():\n",
        "            return \"Strategic recommendation: Focus on ease of use, strong documentation, and enterprise features to compete effectively.\"\n",
        "        else:\n",
        "            return f\"Analyzed: {last_message[:100]}... Recommendation: Implement best practices for scalability and maintainability.\"\n",
        "\n",
        "class CustomTool:\n",
        "    \"\"\"Base class for custom tools\"\"\"\n",
        "    def __init__(self, name: str, description: str):\n",
        "        self.name = name\n",
        "        self.description = description\n",
        "\n",
        "    async def run(self, input_data: str) -> str:\n",
        "        \"\"\"Override this method in subclasses\"\"\"\n",
        "        raise NotImplementedError"
      ],
      "metadata": {
        "id": "5fgGO7auKL2V"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MarketResearchTool(CustomTool):\n",
        "    \"\"\"Custom tool for market research and competitor analysis\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__(\n",
        "            name=\"market_research\",\n",
        "            description=\"Analyzes market trends and competitor information\"\n",
        "        )\n",
        "        self.market_data = {\n",
        "            \"AI_frameworks\": {\n",
        "                \"competitors\": [\"LangChain\", \"CrewAI\", \"AutoGen\", \"Haystack\", \"Semantic Kernel\"],\n",
        "                \"market_size\": \"$2.8B\",\n",
        "                \"growth_rate\": \"42% YoY\",\n",
        "                \"key_trends\": [\"Multi-agent systems\", \"Production deployment\", \"Tool integration\", \"Enterprise adoption\"]\n",
        "            },\n",
        "            \"enterprise_adoption\": {\n",
        "                \"rate\": \"78%\",\n",
        "                \"top_use_cases\": [\"Customer support\", \"Data analysis\", \"Code generation\", \"Document processing\"],\n",
        "                \"challenges\": [\"Reliability\", \"Cost control\", \"Integration complexity\", \"Governance\"]\n",
        "            }\n",
        "        }\n",
        "\n",
        "    async def run(self, query: str) -> str:\n",
        "        \"\"\"Simulate market research based on query\"\"\"\n",
        "        query_lower = query.lower()\n",
        "\n",
        "        if \"competitor\" in query_lower or \"competition\" in query_lower:\n",
        "            data = self.market_data[\"AI_frameworks\"]\n",
        "            return f\"\"\"Market Analysis Results:\n",
        "\n",
        "Key Competitors: {', '.join(data['competitors'])}\n",
        "Market Size: {data['market_size']}\n",
        "Growth Rate: {data['growth_rate']}\n",
        "Key Trends: {', '.join(data['key_trends'])}\n",
        "\n",
        "Recommendation: Focus on differentiating features like simplified deployment, better debugging tools, and enterprise-grade security.\"\"\"\n",
        "\n",
        "        elif \"adoption\" in query_lower or \"enterprise\" in query_lower:\n",
        "            data = self.market_data[\"enterprise_adoption\"]\n",
        "            return f\"\"\"Enterprise Adoption Analysis:\n",
        "\n",
        "Adoption Rate: {data['rate']}\n",
        "Top Use Cases: {', '.join(data['top_use_cases'])}\n",
        "Main Challenges: {', '.join(data['challenges'])}\n",
        "\n",
        "Recommendation: Address reliability and cost control concerns through better monitoring and resource management features.\"\"\"\n",
        "\n",
        "        else:\n",
        "            return \"Market research available for: competitor analysis, enterprise adoption, or specific trend analysis. Please specify your focus area.\""
      ],
      "metadata": {
        "id": "r0BPEx0eKOLK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CodeAnalysisTool(CustomTool):\n",
        "    \"\"\"Custom tool for analyzing code patterns and suggesting improvements\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__(\n",
        "            name=\"code_analysis\",\n",
        "            description=\"Analyzes code structure and suggests improvements\"\n",
        "        )\n",
        "\n",
        "    async def run(self, code_snippet: str) -> str:\n",
        "        \"\"\"Analyze code and provide insights\"\"\"\n",
        "        analysis = {\n",
        "            \"lines\": len(code_snippet.split('\\n')),\n",
        "            \"complexity\": \"High\" if len(code_snippet) > 500 else \"Medium\" if len(code_snippet) > 200 else \"Low\",\n",
        "            \"async_usage\": \"Yes\" if \"async\" in code_snippet or \"await\" in code_snippet else \"No\",\n",
        "            \"error_handling\": \"Present\" if \"try:\" in code_snippet or \"except:\" in code_snippet else \"Missing\",\n",
        "            \"documentation\": \"Good\" if '\"\"\"' in code_snippet or \"'''\" in code_snippet else \"Needs improvement\",\n",
        "            \"imports\": \"Present\" if \"import \" in code_snippet else \"None detected\",\n",
        "            \"classes\": len([line for line in code_snippet.split('\\n') if line.strip().startswith('class ')]),\n",
        "            \"functions\": len([line for line in code_snippet.split('\\n') if line.strip().startswith('def ') or line.strip().startswith('async def ')])\n",
        "        }\n",
        "\n",
        "        suggestions = []\n",
        "        if analysis[\"error_handling\"] == \"Missing\":\n",
        "            suggestions.append(\"Add try-except blocks for error handling\")\n",
        "        if analysis[\"documentation\"] == \"Needs improvement\":\n",
        "            suggestions.append(\"Add docstrings and comments\")\n",
        "        if \"print(\" in code_snippet:\n",
        "            suggestions.append(\"Consider using proper logging instead of print statements\")\n",
        "        if analysis[\"async_usage\"] == \"Yes\" and \"await\" not in code_snippet:\n",
        "            suggestions.append(\"Ensure proper await usage with async functions\")\n",
        "        if analysis[\"complexity\"] == \"High\":\n",
        "            suggestions.append(\"Consider breaking down into smaller functions\")\n",
        "\n",
        "        return f\"\"\"Code Analysis Report:\n",
        "\n",
        "Structure:\n",
        "- Lines of code: {analysis['lines']}\n",
        "- Complexity: {analysis['complexity']}\n",
        "- Classes: {analysis['classes']}\n",
        "- Functions: {analysis['functions']}\n",
        "\n",
        "Quality Metrics:\n",
        "- Async usage: {analysis['async_usage']}\n",
        "- Error handling: {analysis['error_handling']}\n",
        "- Documentation: {analysis['documentation']}\n",
        "\n",
        "Suggestions:\n",
        "{chr(10).join(f\"• {suggestion}\" for suggestion in suggestions) if suggestions else \"• Code looks good! Following best practices.\"}\n",
        "\n",
        "Overall Score: {10 - len(suggestions) * 2}/10\"\"\"\n",
        "\n",
        "class CustomAgent:\n",
        "    \"\"\"Custom agent implementation\"\"\"\n",
        "\n",
        "    def __init__(self, name: str, role: str, instructions: str, tools: List[CustomTool], llm=None):\n",
        "        self.name = name\n",
        "        self.role = role\n",
        "        self.instructions = instructions\n",
        "        self.tools = tools\n",
        "        self.llm = llm or MockChatModel()\n",
        "        self.memory = []\n",
        "\n",
        "    async def run(self, task: str) -> Dict[str, Any]:\n",
        "        \"\"\"Execute agent task\"\"\"\n",
        "        print(f\"🤖 {self.name} ({self.role}) processing task...\")\n",
        "\n",
        "        self.memory.append({\"type\": \"task\", \"content\": task, \"timestamp\": datetime.now()})\n",
        "\n",
        "        task_lower = task.lower()\n",
        "        tool_used = None\n",
        "        tool_result = None\n",
        "\n",
        "        for tool in self.tools:\n",
        "            if tool.name == \"market_research\" and (\"market\" in task_lower or \"competitor\" in task_lower):\n",
        "                tool_result = await tool.run(task)\n",
        "                tool_used = tool.name\n",
        "                break\n",
        "            elif tool.name == \"code_analysis\" and (\"code\" in task_lower or \"analyze\" in task_lower):\n",
        "                tool_result = await tool.run(task)\n",
        "                tool_used = tool.name\n",
        "                break\n",
        "\n",
        "        messages = [\n",
        "            {\"role\": \"system\", \"content\": f\"You are {self.role}. {self.instructions}\"},\n",
        "            {\"role\": \"user\", \"content\": task}\n",
        "        ]\n",
        "\n",
        "        if tool_result:\n",
        "            messages.append({\"role\": \"system\", \"content\": f\"Tool {tool_used} provided: {tool_result}\"})\n",
        "\n",
        "        response = await self.llm.generate(messages)\n",
        "\n",
        "        self.memory.append({\"type\": \"response\", \"content\": response, \"timestamp\": datetime.now()})\n",
        "\n",
        "        return {\n",
        "            \"agent\": self.name,\n",
        "            \"task\": task,\n",
        "            \"tool_used\": tool_used,\n",
        "            \"tool_result\": tool_result,\n",
        "            \"response\": response,\n",
        "            \"success\": True\n",
        "        }"
      ],
      "metadata": {
        "id": "6C6HiU0MKUIn"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class WorkflowMonitor:\n",
        "    \"\"\"Monitor and log workflow events\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.events = []\n",
        "        self.start_time = datetime.now()\n",
        "\n",
        "    def log_event(self, event_type: str, data: Dict[str, Any]):\n",
        "        \"\"\"Log workflow events\"\"\"\n",
        "        timestamp = datetime.now()\n",
        "        self.events.append({\n",
        "            \"timestamp\": timestamp,\n",
        "            \"duration\": (timestamp - self.start_time).total_seconds(),\n",
        "            \"event_type\": event_type,\n",
        "            \"data\": data\n",
        "        })\n",
        "        print(f\"[{timestamp.strftime('%H:%M:%S')}] {event_type}: {data.get('agent', 'System')}\")\n",
        "\n",
        "    def get_summary(self):\n",
        "        \"\"\"Get monitoring summary\"\"\"\n",
        "        return {\n",
        "            \"total_events\": len(self.events),\n",
        "            \"total_duration\": (datetime.now() - self.start_time).total_seconds(),\n",
        "            \"event_types\": list(set([e[\"event_type\"] for e in self.events])),\n",
        "            \"events\": self.events\n",
        "        }\n",
        "\n",
        "class CustomWorkflow:\n",
        "    \"\"\"Custom workflow implementation\"\"\"\n",
        "\n",
        "    def __init__(self, name: str, description: str):\n",
        "        self.name = name\n",
        "        self.description = description\n",
        "        self.agents = []\n",
        "        self.monitor = WorkflowMonitor()\n",
        "\n",
        "    def add_agent(self, agent: CustomAgent):\n",
        "        \"\"\"Add agent to workflow\"\"\"\n",
        "        self.agents.append(agent)\n",
        "        self.monitor.log_event(\"agent_added\", {\"agent\": agent.name, \"role\": agent.role})\n",
        "\n",
        "    async def run(self, tasks: List[str]) -> Dict[str, Any]:\n",
        "        \"\"\"Execute workflow with tasks\"\"\"\n",
        "        self.monitor.log_event(\"workflow_started\", {\"tasks\": len(tasks)})\n",
        "\n",
        "        results = []\n",
        "        context = {\"shared_insights\": []}\n",
        "\n",
        "        for i, task in enumerate(tasks):\n",
        "            agent = self.agents[i % len(self.agents)]\n",
        "\n",
        "            if context[\"shared_insights\"]:\n",
        "                enhanced_task = f\"{task}\\n\\nContext from previous analysis:\\n\" + \"\\n\".join(context[\"shared_insights\"][-2:])\n",
        "            else:\n",
        "                enhanced_task = task\n",
        "\n",
        "            result = await agent.run(enhanced_task)\n",
        "            results.append(result)\n",
        "\n",
        "            context[\"shared_insights\"].append(f\"{agent.name}: {result['response'][:200]}...\")\n",
        "\n",
        "            self.monitor.log_event(\"task_completed\", {\n",
        "                \"agent\": agent.name,\n",
        "                \"task_index\": i,\n",
        "                \"success\": result[\"success\"]\n",
        "            })\n",
        "\n",
        "        self.monitor.log_event(\"workflow_completed\", {\"total_tasks\": len(tasks)})\n",
        "\n",
        "        return {\n",
        "            \"workflow\": self.name,\n",
        "            \"results\": results,\n",
        "            \"context\": context,\n",
        "            \"summary\": self._generate_summary(results)\n",
        "        }\n",
        "\n",
        "    def _generate_summary(self, results: List[Dict[str, Any]]) -> str:\n",
        "        \"\"\"Generate workflow summary\"\"\"\n",
        "        summary_parts = []\n",
        "\n",
        "        for result in results:\n",
        "            summary_parts.append(f\"• {result['agent']}: {result['response'][:150]}...\")\n",
        "\n",
        "        return f\"\"\"Workflow Summary for {self.name}:\n",
        "\n",
        "{chr(10).join(summary_parts)}\n",
        "\n",
        "Key Insights:\n",
        "• Market opportunities identified in AI framework space\n",
        "• Technical architecture recommendations provided\n",
        "• Strategic implementation plan outlined\n",
        "• Multi-agent collaboration demonstrated successfully\"\"\""
      ],
      "metadata": {
        "id": "uzRHNwsUKbb4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "async def advanced_workflow_demo():\n",
        "    \"\"\"Demonstrate advanced multi-agent workflow\"\"\"\n",
        "\n",
        "    print(\"🚀 Advanced Multi-Agent Workflow Demo\")\n",
        "    print(\"=\" * 50)\n",
        "\n",
        "    workflow = CustomWorkflow(\n",
        "        name=\"Advanced Business Intelligence System\",\n",
        "        description=\"Multi-agent system for comprehensive business analysis\"\n",
        "    )\n",
        "\n",
        "    market_agent = CustomAgent(\n",
        "        name=\"MarketAnalyst\",\n",
        "        role=\"Senior Market Research Analyst\",\n",
        "        instructions=\"Analyze market trends, competitor landscape, and business opportunities. Provide data-driven insights with actionable recommendations.\",\n",
        "        tools=[MarketResearchTool()],\n",
        "        llm=MockChatModel()\n",
        "    )\n",
        "\n",
        "    tech_agent = CustomAgent(\n",
        "        name=\"TechArchitect\",\n",
        "        role=\"Technical Architecture Specialist\",\n",
        "        instructions=\"Evaluate technical solutions, code quality, and architectural decisions. Focus on scalability, maintainability, and best practices.\",\n",
        "        tools=[CodeAnalysisTool()],\n",
        "        llm=MockChatModel()\n",
        "    )\n",
        "\n",
        "    strategy_agent = CustomAgent(\n",
        "        name=\"StrategicPlanner\",\n",
        "        role=\"Strategic Business Planner\",\n",
        "        instructions=\"Synthesize market and technical insights into comprehensive strategic recommendations. Focus on ROI, risk assessment, and implementation roadmaps.\",\n",
        "        tools=[],\n",
        "        llm=MockChatModel()\n",
        "    )\n",
        "\n",
        "    workflow.add_agent(market_agent)\n",
        "    workflow.add_agent(tech_agent)\n",
        "    workflow.add_agent(strategy_agent)\n",
        "\n",
        "    tasks = [\n",
        "        \"Analyze the current AI framework market landscape and identify key opportunities for a new multi-agent framework targeting enterprise users.\",\n",
        "        \"\"\"Analyze this code architecture pattern and provide technical assessment:\n",
        "\n",
        "async def multi_agent_workflow():\n",
        "    agents = [ResearchAgent(), AnalysisAgent(), SynthesisAgent()]\n",
        "    context = SharedContext()\n",
        "\n",
        "    for agent in agents:\n",
        "        try:\n",
        "            result = await agent.run(context.get_task())\n",
        "            if result.success:\n",
        "                context.add_insight(result.data)\n",
        "            else:\n",
        "                context.add_error(result.error)\n",
        "        except Exception as e:\n",
        "            logger.error(f\"Agent {agent.name} failed: {e}\")\n",
        "\n",
        "    return context.synthesize_recommendations()\"\"\",\n",
        "        \"Based on the market analysis and technical assessment, create a comprehensive strategic plan for launching a competitive AI framework with focus on multi-agent capabilities and enterprise adoption.\"\n",
        "    ]\n",
        "\n",
        "    print(\"\\n🔄 Executing Advanced Workflow...\")\n",
        "    result = await workflow.run(tasks)\n",
        "\n",
        "    print(\"\\n✅ Workflow Completed Successfully!\")\n",
        "    print(\"=\" * 50)\n",
        "    print(\"📊 COMPREHENSIVE ANALYSIS RESULTS\")\n",
        "    print(\"=\" * 50)\n",
        "    print(result[\"summary\"])\n",
        "\n",
        "    print(\"\\n📈 WORKFLOW MONITORING SUMMARY\")\n",
        "    print(\"=\" * 30)\n",
        "    summary = workflow.monitor.get_summary()\n",
        "    print(f\"Total Events: {summary['total_events']}\")\n",
        "    print(f\"Total Duration: {summary['total_duration']:.2f} seconds\")\n",
        "    print(f\"Event Types: {', '.join(summary['event_types'])}\")\n",
        "\n",
        "    return workflow, result\n",
        "\n",
        "async def simple_tool_demo():\n",
        "    \"\"\"Demonstrate individual tool functionality\"\"\"\n",
        "\n",
        "    print(\"\\n🛠️ Individual Tool Demo\")\n",
        "    print(\"=\" * 30)\n",
        "\n",
        "    market_tool = MarketResearchTool()\n",
        "    code_tool = CodeAnalysisTool()\n",
        "\n",
        "    print(\"Available Tools:\")\n",
        "    print(f\"• {market_tool.name}: {market_tool.description}\")\n",
        "    print(f\"• {code_tool.name}: {code_tool.description}\")\n",
        "\n",
        "    print(\"\\n🔍 Market Research Analysis:\")\n",
        "    market_result = await market_tool.run(\"competitor analysis in AI frameworks\")\n",
        "    print(market_result)\n",
        "\n",
        "    print(\"\\n🔍 Code Analysis:\")\n",
        "    sample_code = '''\n",
        "import asyncio\n",
        "from typing import List, Dict\n",
        "\n",
        "class AgentManager:\n",
        "    \"\"\"Manages multiple AI agents\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.agents = []\n",
        "        self.results = []\n",
        "\n",
        "    async def add_agent(self, agent):\n",
        "        \"\"\"Add agent to manager\"\"\"\n",
        "        self.agents.append(agent)\n",
        "\n",
        "    async def run_all(self, task: str) -> List[Dict]:\n",
        "        \"\"\"Run task on all agents\"\"\"\n",
        "        results = []\n",
        "        for agent in self.agents:\n",
        "            try:\n",
        "                result = await agent.execute(task)\n",
        "                results.append(result)\n",
        "            except Exception as e:\n",
        "                print(f\"Agent failed: {e}\")\n",
        "                results.append({\"error\": str(e)})\n",
        "        return results\n",
        "'''\n",
        "\n",
        "    code_result = await code_tool.run(sample_code)\n",
        "    print(code_result)"
      ],
      "metadata": {
        "id": "WYGvP3xBKgAe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Uyrt_yxHcil",
        "outputId": "b3e3e128-e3a2-4c72-e2d5-8bd1436f4c03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing required packages...\n",
            "✅ beeai-framework installed successfully\n",
            "✅ requests installed successfully\n",
            "✅ beautifulsoup4 installed successfully\n",
            "✅ numpy installed successfully\n",
            "✅ pandas installed successfully\n",
            "✅ pydantic installed successfully\n",
            "Installation complete!\n",
            "⚠️ BeeAI Framework import failed: cannot import name 'ChatModel' from 'beeai_framework' (/usr/local/lib/python3.11/dist-packages/beeai_framework/__init__.py)\n",
            "Falling back to custom implementation...\n",
            "✅ Applied nest_asyncio for Colab compatibility\n",
            "🐝 Advanced BeeAI Framework Tutorial\n",
            "========================================\n",
            "This tutorial demonstrates:\n",
            "• Multi-agent workflows\n",
            "• Custom tool development\n",
            "• Memory management\n",
            "• Event monitoring\n",
            "• Production-ready patterns\n",
            "• Using custom implementation (BeeAI not available)\n",
            "========================================\n",
            "\n",
            "🛠️ Individual Tool Demo\n",
            "==============================\n",
            "Available Tools:\n",
            "• market_research: Analyzes market trends and competitor information\n",
            "• code_analysis: Analyzes code structure and suggests improvements\n",
            "\n",
            "🔍 Market Research Analysis:\n",
            "Market Analysis Results:\n",
            "            \n",
            "Key Competitors: LangChain, CrewAI, AutoGen, Haystack, Semantic Kernel\n",
            "Market Size: $2.8B\n",
            "Growth Rate: 42% YoY\n",
            "Key Trends: Multi-agent systems, Production deployment, Tool integration, Enterprise adoption\n",
            "\n",
            "Recommendation: Focus on differentiating features like simplified deployment, better debugging tools, and enterprise-grade security.\n",
            "\n",
            "🔍 Code Analysis:\n",
            "Code Analysis Report:\n",
            "        \n",
            "Structure:\n",
            "- Lines of code: 27\n",
            "- Complexity: High\n",
            "- Classes: 1\n",
            "- Functions: 3\n",
            "\n",
            "Quality Metrics:\n",
            "- Async usage: Yes\n",
            "- Error handling: Present\n",
            "- Documentation: Good\n",
            "\n",
            "Suggestions:\n",
            "• Consider using proper logging instead of print statements\n",
            "• Consider breaking down into smaller functions\n",
            "\n",
            "Overall Score: 6/10\n",
            "\n",
            "==================================================\n",
            "🚀 Advanced Multi-Agent Workflow Demo\n",
            "==================================================\n",
            "[13:50:03] agent_added: MarketAnalyst\n",
            "[13:50:03] agent_added: TechArchitect\n",
            "[13:50:03] agent_added: StrategicPlanner\n",
            "\n",
            "🔄 Executing Advanced Workflow...\n",
            "[13:50:03] workflow_started: System\n",
            "🤖 MarketAnalyst (Senior Market Research Analyst) processing task...\n",
            "[13:50:03] task_completed: MarketAnalyst\n",
            "🤖 TechArchitect (Technical Architecture Specialist) processing task...\n",
            "[13:50:03] task_completed: TechArchitect\n",
            "🤖 StrategicPlanner (Strategic Business Planner) processing task...\n",
            "[13:50:03] task_completed: StrategicPlanner\n",
            "[13:50:03] workflow_completed: System\n",
            "\n",
            "✅ Workflow Completed Successfully!\n",
            "==================================================\n",
            "📊 COMPREHENSIVE ANALYSIS RESULTS\n",
            "==================================================\n",
            "Workflow Summary for Advanced Business Intelligence System:\n",
            "\n",
            "• MarketAnalyst: Market analysis shows strong growth in AI frameworks with 42% YoY increase. Key competitors include LangChain, CrewAI, and AutoGen....\n",
            "• TechArchitect: Code analysis reveals good structure with async patterns. Consider adding more error handling and documentation....\n",
            "• StrategicPlanner: Market analysis shows strong growth in AI frameworks with 42% YoY increase. Key competitors include LangChain, CrewAI, and AutoGen....\n",
            "\n",
            "Key Insights:\n",
            "• Market opportunities identified in AI framework space\n",
            "• Technical architecture recommendations provided\n",
            "• Strategic implementation plan outlined\n",
            "• Multi-agent collaboration demonstrated successfully\n",
            "\n",
            "📈 WORKFLOW MONITORING SUMMARY\n",
            "==============================\n",
            "Total Events: 8\n",
            "Total Duration: 0.00 seconds\n",
            "Event Types: workflow_completed, workflow_started, agent_added, task_completed\n",
            "\n",
            "🎉 Tutorial Complete!\n",
            "\n",
            "Next Steps:\n",
            "1. Install BeeAI Framework properly: pip install beeai-framework\n",
            "2. Configure your preferred LLM (OpenAI, Anthropic, local models)\n",
            "3. Explore the official BeeAI documentation\n",
            "4. Build custom agents for your specific use case\n",
            "5. Deploy to production with proper monitoring\n"
          ]
        }
      ],
      "source": [
        "async def main():\n",
        "    \"\"\"Main demo function\"\"\"\n",
        "\n",
        "    print(\"🐝 Advanced BeeAI Framework Tutorial\")\n",
        "    print(\"=\" * 40)\n",
        "    print(\"This tutorial demonstrates:\")\n",
        "    print(\"• Multi-agent workflows\")\n",
        "    print(\"• Custom tool development\")\n",
        "    print(\"• Memory management\")\n",
        "    print(\"• Event monitoring\")\n",
        "    print(\"• Production-ready patterns\")\n",
        "\n",
        "    if BEEAI_AVAILABLE:\n",
        "        print(\"• Using real BeeAI Framework\")\n",
        "    else:\n",
        "        print(\"• Using custom implementation (BeeAI not available)\")\n",
        "\n",
        "    print(\"=\" * 40)\n",
        "\n",
        "    await simple_tool_demo()\n",
        "\n",
        "    print(\"\\n\" + \"=\"*50)\n",
        "    await advanced_workflow_demo()\n",
        "\n",
        "    print(\"\\n🎉 Tutorial Complete!\")\n",
        "    print(\"\\nNext Steps:\")\n",
        "    print(\"1. Install BeeAI Framework properly: pip install beeai-framework\")\n",
        "    print(\"2. Configure your preferred LLM (OpenAI, Anthropic, local models)\")\n",
        "    print(\"3. Explore the official BeeAI documentation\")\n",
        "    print(\"4. Build custom agents for your specific use case\")\n",
        "    print(\"5. Deploy to production with proper monitoring\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    try:\n",
        "        import nest_asyncio\n",
        "        nest_asyncio.apply()\n",
        "        print(\"✅ Applied nest_asyncio for Colab compatibility\")\n",
        "    except ImportError:\n",
        "        print(\"⚠️ nest_asyncio not available - may not work in some environments\")\n",
        "\n",
        "    asyncio.run(main())"
      ]
    }
  ]
}